The constituents of many complex systems are characterized by non-trivial connectivity
patterns and dynamical processes that are well captured by network models. However, most …

Complex systems are characterized by many interacting units that give rise to emergent
behavior. A particularly advantageous way to study these systems is through the analysis of …

Community detection is one of the most popular researches in a variety of complex systems,
ranging from biology to sociology. In recent years, there's an increasing focus on the rapid …

Complex systems are often characterized by distinct types of interactions between the same
entities. These can be described as a multilayer network where each layer represents one …

Real-world systems in epidemiology, social sciences, power transportation, economics and
engineering are often described as multilayer networks. Here we first define and compute …

Numerous centrality measures have been developed to quantify the importances of nodes in
time-independent networks, and many of them can be expressed as the leading eigenvector …

The field of neuroscience is facing an unprecedented expanse in the volume and diversity of
available data. Traditionally, network models have provided key insights into the structure …

Multilayer networks are a useful data structure for simultaneously capturing multiple types of
relationships between a set of nodes. In such networks, each relational definition gives rise …

Any physical system can be viewed from the perspective that information is implicitly
represented in its state. However, the quantification of this information when it comes to …

We consider multi-layer network data where the relationships between pairs of elements are
reflected in multiple modalities, and may be described by multivariate or even high …